TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhys.15.3.107
TI  - Unweighting multijet event generation using factorisation-aware neural networks
PY  - 2023/09/21
UR  - https://www.scipost.org/SciPostPhys.15.3.107
JF  - SciPost Physics
JA  - SciPost Phys.
VL  - 15
IS  - 3
SP  - 107
A1  - Janßen, Timo
AU  - Maître, Daniel
AU  - Schumann, Steffen
AU  - Siegert, Frank
AU  - Truong, Henry
AB  - In this article we combine a recently proposed method for factorisation-aware matrix element surrogates with an unbiased unweighting algorithm. We show that employing a sophisticated neural network emulation of QCD multijet matrix elements based on dipole factorisation can lead to a drastic acceleration of unweighted event generation. We train neural networks for a selection of partonic channels contributing at the tree-level to $Z+4,5$ jets and $t\bar{t}+3,4$ jets production at the LHC which necessitates a generalisation of the dipole emulation model to include initial state partons as well as massive final state quarks. We also present first steps towards the emulation of colour-sampled amplitudes. We incorporate these emulations as fast and accurate surrogates in a two-stage rejection sampling algorithm within the SHERPA Monte Carlo that yields unbiased unweighted events suitable for phenomenological analyses and post-processing in experimental workflows, e.g. as input to a time-consuming detector simulation. For the computational cost of unweighted events we achieve a reduction by factors between $16$ and $350$ for the considered channels.
ER  -

@Article{10.21468/SciPostPhys.15.3.107,
	title={{Unweighting multijet event generation using factorisation-aware neural networks}},
	author={Timo Janßen and Daniel Maître and Steffen Schumann and Frank Siegert and Henry Truong},
	journal={SciPost Phys.},
	volume={15},
	pages={107},
	year={2023},
	publisher={SciPost},
	doi={10.21468/SciPostPhys.15.3.107},
	url={https://scipost.org/10.21468/SciPostPhys.15.3.107},
}